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会議論文

Infinite Mixtures of Gaussian Process Experts

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Rasmussen, CE
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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引用

Rasmussen, C. (2002). Infinite Mixtures of Gaussian Process Experts.

要旨

We present an extension to the Mixture of Experts (ME) model, where the individual experts are Gaussian Process (GP) regression models. Using a input-dependent adaptation of the Dirichlet Process, we implement a gating network for an infinite number of Experts. Inference in this model may be done efficiently using a Markov Chain relying on Gibbs sampling. The model allows the effective covariance function to vary with the inputs, and may handle large datasets -- thus potentially overcoming two of the biggest hurdles with GP models. Simulations show the viability of this approach.